Neural signals are extremely variable. Understanding the variability and harnessing its rich content lie at the very heart of contemporary neuroscience. In this application we propose to tackle 1 aspect of this vast field: estimation of event related field potential signals on a trial-by-trial basis and applying the estimated single trial event related parameters to address questions related to functions of neural systems. There are 2 Specific Aims. In Aim 1 we propose to further develop and thoroughly validate a single-trial analysis methodology termed differentially variable component analysis (dVCA). In Aim 2 we propose to apply the methodology to analyze local field potentials from 2 existing datasets: 1 from macaque monkeys performing a visuomotor pattern discrimination task and the other from macaque monkeys performing an intermodal (visual versus auditory) selective attention task. The first dataset is unique in that it consists of local field potentials simultaneously recorded from up to 16 bipolar intracortical electrodes chronically implanted in one hemisphere, and is therefore ideally suited for addressing issues related to timing and large-scale networking of neural activations and their task relevance. The second dataset is unique in consisting of local field potentials recorded simultaneously from multiple contacts along a linear electrode spanning all the cortical layers in the primary visual cortex. Our goal is to study how visual information processing is modulated by attention.